Assignment 03

Author

Affiliation

Ryan Martin

Boston University

from pyspark.sql import SparkSession
import pandas as pd
import plotly.express as px
import plotly.io as pio
pio.renderers.default = "svg"
import re
import numpy as np
import plotly.graph_objects as go
from pyspark.sql.functions import col, split, explode, regexp_replace, transform, when
from pyspark.sql import functions as F
from pyspark.sql.functions import col, monotonically_increasing_id

np.random.seed(42)

pio.renderers.default = "notebook"

# Initialize Spark Session
spark = SparkSession.builder.appName("./data/LightcastData").getOrCreate()

# Load Data
df = spark.read.option("header", "true").option("inferSchema", "true").option("multiLine","true").option("escape", "\"").csv("./data/lightcast_job_postings.csv")

# Show Schema and Sample Data
#print("---This is Diagnostic check, No need to print it in the final doc---")

#df.printSchema() # comment this line when rendering the submission
#df.show(5)

WARNING: Using incubator modules: jdk.incubator.vector
Using Spark's default log4j profile: org/apache/spark/log4j2-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
25/09/22 04:31:22 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
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1 1. Data Preperation

df = df.withColumn("SALARY", col("SALARY").cast("float")) \
      .withColumn("SALARY_FROM", col("SALARY_FROM").cast("float")) \
      .withColumn("SALARY_TO", col("SALARY_TO").cast("float")) \
      .withColumn("MIN_YEARS_EXPERIENCE", col("MIN_YEARS_EXPERIENCE").cast("float")) \
      .withColumn("MAX_YEARS_EXPERIENCE", col("MAX_YEARS_EXPERIENCE").cast("float")) \
      
def compute_median(sdf, col_name):
  q = sdf.approxQuantile(col_name, [0.5], 0.01)
  return q[0] if q else None

median_from = compute_median(df, "SALARY_FROM")
median_to = compute_median(df, "SALARY_TO")
median_salary = compute_median(df, "SALARY")

print("Medians:", median_from, median_to, median_salary)

df = df.fillna({
      "SALARY_FROM": median_from,
      "SALARY_TO": median_to,
      "SALARY": median_salary
})

df= df.withColumn("Average_Salary", (col("SALARY_FROM") + col("SALARY_TO")) / 2)


export_cols = [
    "EMPLOYMENT_TYPE_NAME",
    "EDUCATION_LEVELS_NAME",
    "REMOTE_TYPE_NAME",
    "MAX_YEARS_EXPERIENCE",
    "Average_Salary",
    "SALARY",
    "LOT_V6_SPECIALIZED_OCCUPATION_NAME"
] 

df_selected = df.select(*export_cols)

pdf = df_selected.toPandas()
pdf.to_csv("./data/lightcast_cleaned.csv", index=False)


print("Data Cleaning Complete. Rows retained:", len(pdf))

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Medians: 87295.0 130042.0 115024.0
Data Cleaning Complete. Rows retained: 72498

2 2. Salary Distribution by Industry and Employment Type

import pandas as pd


pdf = df.filter(df["SALARY"] > 0).select("EMPLOYMENT_TYPE_NAME", "SALARY").toPandas()
import re
pdf["EMPLOYMENT_TYPE_NAME"] = pdf["EMPLOYMENT_TYPE_NAME"].apply(lambda x: re.sub(r"[^\x00-\x7F]+", "", x) if isinstance(x, str) else "")

pdf = pdf[pdf["EMPLOYMENT_TYPE_NAME"] != ""]

median_salaries = pdf.groupby("EMPLOYMENT_TYPE_NAME")["SALARY"].median()
median_salaries.head()

sorted_employment_types = median_salaries.sort_values(ascending=False).index

pdf["EMPLOYMENT_TYPE_NAME"] = pd.Categorical(
    pdf["EMPLOYMENT_TYPE_NAME"],
    categories=sorted_employment_types,
    ordered=True
)
fig = px.box(
    pdf,
    x = "EMPLOYMENT_TYPE_NAME",
    y = "SALARY",
    title = "Salary Distribution by Employment Type",
    color_discrete_sequence=["black"],
    boxmode = "group",
    points = "all",
)

fig.update_layout(
    title=dict(
        text="Salary Distribution by Employment Type",
        font=dict(size=30, family="Arial", color="black", weight = "bold")
    ),
    
    xaxis=dict(
        title=dict(text="Employment Type", font=dict(size=14, family = "Arial", color = "black", weight = "bold")),
        tickangle=0, 
        tickfont=dict(size=12, family = "Arial", color = "black", weight = "bold"),
        showline = True,
        linewidth=2,
        linecolor="black",
        mirror=True,
        showgrid=False,
        categoryorder="array",
        categoryarray=sorted_employment_types.tolist()
    ),
    yaxis=dict(title=dict(text="Salary (K $)", font=dict(size=14, family = "Arial", color = "black", weight = "bold")),
        tickvals=[0, 50000, 100000, 150000, 200000, 250000, 300000, 350000, 400000, 450000, 500000],
        ticktext=["0", "50K", "100K", "150K", "200K", "250K", "300K", "350K", "400K", "450K", "500K"],
        tickfont=dict(size=12, family = "Arial", color = "black", weight = "bold"),
        showline = True,
        linewidth=2,
        linecolor="black",
        mirror=True,
        showgrid=False,
        gridcolor="lightgray",
        gridwidth=0.5,
    ),
    font=dict(family="Arial", size = 12, color = "black"),
    boxgap=0.7,
    plot_bgcolor="white",
    paper_bgcolor="white",
    showlegend=False,
    height=500,
    width=800,


)



fig.show()
fig.write_html("output/Q1.html")
fig.write_image("output/Q1.svg", width=850, height=500, scale=1)

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